You are not logged in.

Energy consumption prediction based on time-series models for CPU-intensive activities in the cloud

Li, Juan, Liu, Xiao, Zhao, Zhou and Liu, Jin 2015, Energy consumption prediction based on time-series models for CPU-intensive activities in the cloud, in ICA3PP 2015 : Proceedings of the algorithms and architectures for parallel processing 2015 conference, part 4, Springer, Berlin, Germany, pp. 756-769, doi: 10.1007/978-3-319-27140-8_52.

Attached Files
Name Description MIMEType Size Downloads

Title Energy consumption prediction based on time-series models for CPU-intensive activities in the cloud
Author(s) Li, Juan
Liu, Xiao
Zhao, Zhou
Liu, Jin
Conference name Algorithms and architectures for parallel processing. Conference (15th : 2015 : Zhangjiajie, China)
Conference location Zhangjiajie, China
Conference dates 18-20 Nov. 2015
Title of proceedings ICA3PP 2015 : Proceedings of the algorithms and architectures for parallel processing 2015 conference, part 4
Editor(s) Wang, Guojun
Zomaya, Albert
Perez, Gregorio M.
Li, Kenli
Publication date 2015
Series Lecture notes in computer science 9531
Conference series Algorithms and architectures for parallel processing
Start page 756
End page 769
Total pages 14
Publisher Springer
Place of publication Berlin, Germany
Keyword(s) Cloud computing
Energy consumption prediction
Time-series model
Time-series segmentation
Summary Due to the increasing energy consumption in cloud data centers, energy saving has become a vital objective in designing the underlying cloud infrastructures. A precise energy consumption model is the foundation of many energy-saving strategies. This paper focuses on exploring the energy consumption of virtual machines running various CPU-intensive activities in the cloud server using two types of models: traditional time-series models, such as ARMA and ES, and time-series segmentation models, such as sliding windows model and bottom-up model. We have built a cloud environment using OpenStack, and conducted extensive experiments to analyze and compare the prediction accuracy of these strategies. The results indicate that the performance of ES model is better than the ARMA model in predicting the energy consumption of known activities. When predicting the energy consumption of unknown activities, sliding windows segmentation model and bottom-up segmentation model can all have satisfactory performance but the former is slightly better than the later.
ISBN 9783319271408
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-27140-8_52
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084929

Document type: Conference Paper
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 54 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Fri, 15 Jul 2016, 12:00:38 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.